Are artificial intelligence learnings or trainings transferable from one agent to the other?
The simplest case is copying the software. That is instant duplication of the learnings. In a similar, but less trivial way you can adjust pretrained neural network classifiers to new datasets by simply re-initializing the last layer.

It gets more interesting when you have multiple agents and you want to combine their knowledge. This can be done with many techniques; the simplest is averaging the weights of identical neural network architectures. One example of this combined learning happened is Google Has a Room Full of Robot Arms Learning Hand-Eye Coordination. The relevant paper is Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection.

The most complicated case, where you want to transfer just the gist and prevent forgetting is -- up to my knowledge, and I'm relatively certain it's outdated in that area -- unsolved. The problem is post-hoc explanations. Humans decide to do something, observe the outcome and then explain why they did something / why it was good to do it. So we automatically generate hypotheses about the world. And sometimes we are able to formulate them in a way that others can understand them. That is not possible with current learning machines. We don't know yet how to abstract automatically from arbitrary problems.